Estimated crime rates are ~134% higher in London’s most
deprived neighborhoods

Per 10,000 residents by income deprivation decile (2024). Rates use an equal-population estimate (~833k residents per decile; interpret comparatively)

Analyzing London’s crime gradient across income deprivation levels using per capita rates. The visualization reveals that crime rates in the most deprived neighborhoods are ~134% higher than in the least deprived areas, even after population adjustment.

Original

The original visualization comes from London crimes by income deprivation decile

Original visualization

Makeover

Figure 1: Area chart showing estimated crime rates per 10,000 residents across London’s income deprivation deciles, from most deprived (1) to least deprived (10). Crime rates are much higher in more deprived areas: the most deprived decile has about 1,656 crimes per 10,000 residents, which is roughly 134% higher than the least deprived decile at about 707. Rates peak around the second and third-most deprived deciles and then decline steadily toward the least deprived areas. Rates are based on equal-population estimates per decile and should be interpreted comparatively rather than as exact values.

Methodology & Data Sources

Population Estimates & Per Capita Calculations

This analysis uses per capita crime rates (crimes per 10,000 residents) rather than raw counts to enable fair comparison across income deprivation deciles. Since population data aggregated by income deprivation decile was not readily available, we estimated populations based on:

• London LSOA count: 4,835 Lower Layer Super Output Areas (London Datastore)
• Average LSOA population: 1,722 residents
• Estimated population per decile: ~833,000 residents (assuming roughly equal distribution)
• Total estimated: 8.33 million (vs. 8.9 million actual London population, 2021 Census)

Note: Trust for London uses London-rebased income deprivation deciles, ranking only London’s 4,835 LSOAs into deciles (not England-wide rankings).

Critical Limitations & Assumptions

Key Assumptions About Population Estimates

1. Equal Distribution Assumption - We assume approximately equal population across all 10 income deprivation deciles - Each decile: ~483-484 LSOAs × 1,722 residents = ~833,000 people - This is a simplification - actual populations likely vary by decile

2. Why Estimates Were Necessary - IoD2025 File 7 (official population denominators) was not accessible - No readily available population data aggregated by income deprivation decile - Per capita analysis requires population denominators for meaningful comparison

3. Validity of Findings - Under equal-population assumption: 134% difference persists (raw counts = per capita rates) - This suggests the crime disparity is real, not merely a population artifact - More deprived areas may have different population density than affluent areas

4. Interpretation Guidance - Per capita rates shown are based on estimated, not actual, populations - True rates may vary if actual population distribution differs significantly - Treat values as comparative rather than exact - All visualizations note this limitation in titles/subtitles/captions

Data Preparation Pipeline

The CSV files used in this analysis were generated using a Python preprocessing script that calculated per capita crime rates with population estimates.

Reproducibility: Data Preparation Steps

To reproduce the analysis from raw data:

1. Run the Python preprocessing script:
   • Script: mm_2025_week49_prep.py
   • Input: mom_week49_data.csv (raw crime counts from data.world)
   • Output: Two tidy CSV files with per capita rates
2. Run the R visualization script (this document)
   • Uses the pre-calculated per capita rates
   • Creates the final visualization

The Python script documents: - Population estimation methodology (4,835 LSOAs × 1,722 avg residents) - Per capita rate calculations (crimes per 10,000 residents)
- All assumptions and limitations (detailed above)

Repository: MakeoverMonday/2025/Week_49

Data Sources

• Crime Data: Trust for London, DataPoliceUK (January-December 2024)
• Deprivation Rankings: Indices of Multiple Deprivation 2025 (IoD2025)
• Population Reference: London Datastore - LSOA Atlas
• Challenge Data: data.world - MakeoverMonday Week 49

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
  tidyverse,     # Easily Install and Load the 'Tidyverse'
  janitor,       # Simple Tools for Examining and Cleaning Dirty Data
  skimr,         # Compact and Flexible Summaries of Data
  scales,        # Scale Functions for Visualization
  ggtext,        # Improved Text Rendering Support for 'ggplot2'
  showtext,      # Using Fonts More Easily in R Graphs
  glue           # Interpreted String Literals
)
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 10,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|

total_crimes_by_decile <- read_csv(
   here::here("data/MakeoverMonday/2025/london_total_crimes_by_deprivation.csv")) |>
  clean_names()
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(total_crimes_by_decile)
skim(total_crimes_by_decile) |> summary()
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

### |-  data wrangling ----
# Baseline = least deprived (decile 10)
baseline_rate <- total_crimes_by_decile |>
  filter(decile_num == 10) |>
  pull(total_crimes_per_10k)

crime_gradient <- total_crimes_by_decile |>
  mutate(
    baseline_rate = baseline_rate,
    rate_ratio = total_crimes_per_10k / baseline_rate,
    pct_difference = ((total_crimes_per_10k - baseline_rate) / baseline_rate) * 100
  )

# Values for annotations
most_deprived_rate <- crime_gradient |> filter(decile_num == 1)  |> pull(total_crimes_per_10k)
least_deprived_rate <- crime_gradient |> filter(decile_num == 10) |> pull(total_crimes_per_10k)
overall_pct_diff <- crime_gradient |> filter(decile_num == 1) |> pull(pct_difference)

# Identify peak decile 
peak_decile <- crime_gradient |>
  slice_max(total_crimes_per_10k, n = 1, with_ties = FALSE) |>
  pull(decile_num)

peak_rate <- crime_gradient |>
  filter(decile_num == peak_decile) |>
  pull(total_crimes_per_10k)
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(
  palette = list(
    col_primary = "#1E3A5F",
    col_danger  = "#B5532F"
  )
)

### |-  Main titles ----
title_text <- "Estimated crime rates are ~134% higher in London’s most<br>deprived neighborhoods"

subtitle_text <- str_glue(
  "Per 10,000 residents by income deprivation decile (2024). ",
  "Rates use an equal-population estimate (~833k residents<br>per decile; interpret comparatively). "
)

caption_text <- create_social_caption_02_mm(
  mm_year = 2025, mm_week = 49,
  source_text = str_glue(
    "**Crime Data:** Trust for London, DataPoliceUK (2024) | ",
    "**Deprivation Rankings:** Indices of Multiple Deprivation 2025<br>"
  ),
  note_text = str_glue(
    "**Methodology:** Rates are normalized per 10,000 residents assuming equal population per decile (~833k). ",
    "Because true populations by decile may vary,<br>treat values as **comparative** rather than exact. ",
    "London-rebased deciles rank neighborhoods within London only.<br>"
  )
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.5), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "right",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10), family = fonts$subtitle,
      color = "gray40" 
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10), family = fonts$subtitle,
      color = "gray40" 
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"  
    ),
    axis.text.y = element_markdown(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |-  main plot ----
p <- 
  ggplot(crime_gradient, aes(x = decile_num, y = total_crimes_per_10k)) +
  # Geoms
  geom_area(fill = colors$palette$col_primary, alpha = 0.20) +
  geom_line(color = colors$palette$col_primary, linewidth = 1.5) +
  geom_point(color = colors$palette$col_primary, size = 3) +
  geom_point(
    data = crime_gradient |> filter(decile_num %in% c(1, 10)),
    size = 6, color = colors$palette$col_danger
  ) +
  # Annotations
  annotate(
    "text",
    x = 1.1, y = 1350,
    label = glue(
      "Most deprived areas (Decile 1):\n",
      "{comma(round(most_deprived_rate))} crimes per 10k\n",
      "({round(overall_pct_diff)}% higher than least deprived)"
    ),
    size = 3.6, hjust = 0,
    fontface = "bold", lineheight = 1.1,
    color = colors$palette$col_danger
  ) +
  annotate(
    "text",
    x = 10.0, y = 1050,
    label = glue(
      "Least deprived areas (Decile 10):\n",
      "{comma(round(least_deprived_rate))} crimes per 10k\n",
      "(Baseline for comparison)"
    ),
    size = 3.6, hjust = 1,
    fontface = "bold", lineheight = 1.1,
    color = colors$palette$col_primary
  ) +
  annotate(
    "text",
    x = peak_decile, y = 2700,
    label = "Peak occurs around deciles 2–3",
    size = 3.2,
    fontface = "bold",
    color = colors$palette$col_primary,
    vjust = -0.2
  ) +
  annotate(
    "text",
    x = 5.5, y = 200,
    label = str_wrap(
      "Note: Income deprivation deciles rank London's neighborhoods from most (1) to least (10) deprived. Each decile represents ~10% of areas.",
      width = 86
    ),
    size = 3,
    color = "gray30",
    lineheight = 1.1,
    hjust = 0.5
  ) +
  # Scales
  scale_x_continuous(
    breaks = 1:10,
    labels = c(
      "1\nMost\nDeprived", "2", "3", "4", "5", "6", "7", "8", "9",
      "10\nLeast\nDeprived"
    )
  ) +
  scale_y_continuous(
    labels = comma,
    limits = c(0, 2700),
    breaks = seq(0, 2500, 500)
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Income Deprivation Decile",
    y = "Crimes per 10,000 Residents"
  ) +
  # Theme
  theme(
    plot.title = element_markdown(
      size = rel(1.9),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.15,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.88),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.88),
      lineheight = 1.5,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.65),
      family = fonts$subtitle,
      color = colors$caption,
      hjust = 0,
      lineheight = 1.4,
      margin = margin(t = 20, b = 5)
    ),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.y = element_blank(),
    panel.grid.major.y = element_line(color = "gray90", linewidth = 0.3),
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 10, 
  height = 8
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      glue_1.8.0      showtext_0.9-7  showtextdb_3.0 
 [5] sysfonts_0.8.9  ggtext_0.1.2    scales_1.3.0    skimr_2.1.5    
 [9] janitor_2.2.0   lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.5.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3  
[13] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     repr_1.1.7        codetools_0.2-20  snakecase_0.11.1 
[21] htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3      pillar_1.9.0     
[25] camcorder_0.1.0   magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1 
[29] digest_0.6.37     stringi_1.8.4     rsvg_2.6.1        rprojroot_2.0.4  
[33] fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1  cli_3.6.4        
[37] magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4        withr_3.0.2      
[41] bit64_4.5.2       timechange_0.3.0  rmarkdown_2.29    bit_4.5.0        
[45] ragg_1.3.3        hms_1.1.3         evaluate_1.0.1    knitr_1.49       
[49] markdown_1.13     rlang_1.1.6       gridtext_0.1.5    Rcpp_1.0.13-1    
[53] xml2_1.3.6        renv_1.0.3        vroom_1.6.5       svglite_2.1.3    
[57] rstudioapi_0.17.1 jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_49.qmd.

For the full repository, click here.

10. References

Expand for References

1. Data:

   • Makeover Monday 2025 Week 49: London crimes by income deprivation decile

2. Article

   • London crimes by income deprivation decile

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils